1 S-Matrix and the Grid Geoffrey Fox Professor of Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington.

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S-Matrix and the GridGeoffrey Fox

Professor of Computer Science, Informatics, Physics

Pervasive Technology Laboratories

Indiana University Bloomington IN 47401

December 12 2003

[email protected]

http://www.infomall.org

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S-Matrix and PWA We need an amplitude analysis to find most “interesting” resonances If this makes sense, we are effectively parameterizing photon-Reggeon

amplitude with resonance at “top” vertex in full (123 in diagram) or partial (12, 23, 31) channel

• Complicated as off diagonal, one “fake” particle and often more than 2 final particles

This requires a lot of approximations whose effect can be estimated with S-Matrix Theory

• Analyticity, Unitarity, Crossing, Regge Theory, Spin formalism, Duality, Finite Energy Sum Rules

1

2

3

ReggeonExchange forProduction

Exchange

Target

Regge in Top Vertex

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Some Lessons from the past I All confusing effects exist and no fundamental (correct) way to

remove. So one should:• Minimize effect of the hard (insoluble) problems such as

“particles from wrong vertex”, “unestimatable exchange effects” sensitive to slope of unclear Regge trajectories, absorption etc.

• Carefully identify where effects are “additive” and where confusingly overlapping

Note many of effects are intrinsically MORE important in multiparticle case than in relatively well studied π N π N

Try to estimate impact of uncertainties from each effect on results• It would be very helpful to get systematic very high statistic

studies of relatively clean cases where spectroscopy may be less interesting but one can examine uncertainties

• Possibilities are A1 A2 A3 B1 peripherally produced and even π N π π N; K or π beams good

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S-Matrix Approach S-Matrix ideas that work reasonably include: Regge theory for production process Two-component duality adding Regge dual to Regge to background

dual to the Pomeron• Can help to identify if a resonance is classic qq or exotic

Use of Regge exchange at top vertex to estimate high partial waves in amplitude analysis

Finite Energy Sum Rules for top vertex as constraints on low mass amplitudes and most quantitative way of linking high and low masses

Ignore Regge Cuts in Production Unitarity effects not included directly due to duality double counting

qq

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Investigate Uncertainties There are several possible sources of error

• Errors in Quasi 2-body and limited number of amplitudes approximation

• Unitarity (final state interactions)• Errors in the two-component duality picture• Exotic particles are produced and are just different• Photon beams, π exchange or some other “classic effect” not

present in original πN analyses behaves unexpectedly• Failure of quasi two body approximation• Regge cuts cannot be ignored• Background from other channels

Develop tests for these in both “easy” cases (such as “old” meson beam data) and in photon beam data at Jefferson laboratory• Investigate all effects on any interesting result from PWA

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Grid Computing: Making The Global Infrastructure a Reality

Note book withFran Berman andAnthony J.G. Hey,

ISBN: 0-470-85319-0 Hardcover 1080 Pages Published March 2003 http://www.grid2002.org I had more fun in days gone by; no

more do I write “Skeletons in the Regge

Cupboard” or “The Importance of being an

Amplitude”

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Some Further Links A talk on Grid and e-Science was webcast in an Oracle

technology serieshttp://webevents.broadcast.com/techtarget/Oracle/100303/index.asp?loc=10

See also the “Gap Analysis” survey of Grid technologyhttp://grids.ucs.indiana.edu/ptliupages/publications/GapAnalysis30June03v2.pdf

This presentation is at http://grids.ucs.indiana.edu/ptliupages/presentations

Next Semester – course on “e-Science and the Grid” given by Access Grid

Write up for May Conference describes proposed Physics Strategyhttp://grids.ucs.indiana.edu/ptliupages/publications/gluonic_gcf.pdfhttp://grids.ucs.indiana.edu/ptliupages/presentations/pwamay03.ppt

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e-Business e-Science and the Grid e-Business captures an emerging view of corporations as

dynamic virtual organizations linking employees, customers and stakeholders across the world. • The growing use of outsourcing is one example

e-Science is the similar vision for scientific research with international participation in large accelerators, satellites or distributed gene analyses.

The Grid integrates the best of the Web, traditional enterprise software, high performance computing and Peer-to-peer systems to provide the information technology infrastructure for e-moreorlessanything.

A deluge of data of unprecedented and inevitable size must be managed and understood.

People, computers, data and instruments must be linked. On demand assignment of experts, computers, networks and

storage resources must be supported

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What is a High Performance Computer? We might wish to consider three classes of multi-node computers 1) Classic MPP with microsecond latency and scalable internode

bandwidth (tcomm/tcalc ~ 10 or so) 2) Classic Cluster which can vary from configurations like 1) to 3)

but typically have millisecond latency and modest bandwidth 3) Classic Grid or distributed systems of computers around the

network• Latencies of inter-node communication – 100’s of milliseconds

but can have good bandwidth All have same peak CPU performance but synchronization costs

increase as one goes from 1) to 3) Cost of system (dollars per gigaflop) decreases by factors of 2 at

each step from 1) to 2) to 3) One should NOT use classic MPP if class 2) or 3) suffices unless

some security or data issues dominates over cost-performance One should not use a Grid as a true parallel computer – it can

link parallel computers together for convenient access etc.

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Sources of Grid Technology Grids support distributed collaboratories or virtual

organizations integrating concepts from The Web Agents Distributed Objects (CORBA Java/Jini COM) Globus, Legion, Condor, NetSolve, Ninf and other High

Performance Computing activities Peer-to-peer Networks With perhaps the Web and P2P networks being the most

important for “Information Grids” and Globus for “Compute Grids”

Service Architecture based on Web Services most critical feature

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Raw (HPC) Resources

Middleware

Database

PortalServices

SystemServices

SystemServices

SystemServices

Application Service

SystemServices

SystemServices

UserServices

“Core”Grid

Typical Grid Architecture

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A typical Web Service In principle, services can be in any language (Fortran .. Java ..

Perl .. Python) and the interfaces can be method calls, Java RMI Messages, CGI Web invocations, totally compiled away (inlining)

The simplest implementations involve XML messages (SOAP) and programs written in net friendly languages like Java and Python

PaymentCredit Card

WarehouseShippingcontrol

WSDL interfaces

WSDL interfaces

Security CatalogPortalService

Web Services

Web Services

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What is Happening? Grid ideas are being developed in (at least) two

communities• Web Service – W3C, OASIS• Grid Forum (High Performance Computing, e-Science)

Service Standards are being debated Grid Operational Infrastructure is being deployed Grid Architecture and core software being developed Particular System Services are being developed

“centrally” – OGSA framework for this in Lots of fields are setting domain specific standards and

building domain specific services There is a lot of hype Grids are viewed differently in different areas

• Largely “computing-on-demand” in industry (IBM, Oracle, HP, Sun)

• Largely distributed collaboratories in academia

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Technical Activities of Note Look at different styles of Grids such as Autonomic (Robust

Reliable Resilient) New Grid architectures hard due to investment required Critical Services Such as

• Security – build message based not connection based• Notification – event services• Metadata – Use Semantic Web, provenance• Databases and repositories – instruments, sensors• Computing – Submit job, scheduling, distributed file

systems• Visualization, Computational Steering• Fabric and Service Management• Network performance

Program the Grid – Workflow Access the Grid – Portals, Grid Computing Environments

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Issues and Types of Grid Services 1) Types of Grid

• R3• Lightweight• P2P• Federation and Interoperability

2) Core Infrastructure and Hosting Environment

• Service Management• Component Model• Service wrapper/Invocation • Messaging

3) Security Services• Certificate Authority• Authentication• Authorization• Policy

4) Workflow Services and Programming Model

• Enactment Engines (Runtime)• Languages and Programming• Compiler• Composition/Development

5) Notification Services 6) Metadata and Information Services

• Basic including Registry• Semantically rich Services and meta-

data• Information Aggregation (events)• Provenance

7) Information Grid Services• OGSA-DAI/DAIT• Integration with compute resources• P2P and database models

8) Compute/File Grid Services• Job Submission• Job Planning Scheduling

Management• Access to Remote Files, Storage and

Computers• Replica (cache) Management• Virtual Data• Parallel Computing

9) Other services including• Grid Shell• Accounting• Fabric Management• Visualization Data-mining and

Computational Steering• Collaboration

10) Portals and Problem Solving Environments

11) Network Services• Performance• Reservation• Operations

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OGSA OGSI & Hosting Environments Start with Web Services in a hosting environment Add OGSI to get a Grid service and a component model Add OGSA to get Interoperable Grid “correcting” differences in base platform

and adding key functionalities

OGSI on Web Services

Broadly applicable services: registry,authorization, monitoring, data

access, etc., etc.

Hosting Environment for WS

More specialized services: datareplication, workflow, etc., etc.

Domain -specific services

Network

OGSAEnvironment

Possibly OGSA

Not OGSA

Given to us from on high

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Integration of Data and Filters One has the OGSA-DAI Data repository interface combined

with WSDL of the (Perl, Fortran, Python …) filter User only sees WSDL not data syntax Some non-trivial issues as to where the filtering compute

power is• Microsoft says filter next to data

DBFilter

WSDL

Of Filter

OGSA-DAI

Interface

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Data

Technology Components of (Services in)a Computing Grid

1: Job Management Service(Grid Service Interface to user or program client)

2: Schedule and control Execution

1: Plan Execution 4: Job Submittal

Remote Grid ServiceRemote Grid Service

6: File andStorage Access

3: Access to Remote Computers

Data

7: CacheData

Replicas5: Data Transfer

10: JobStatus

8: VirtualData

9: Grid MPI

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Grid Strategy LHC Computing will be very well established and

handling 10-100 times as much data as GlueX when we need to go into production

GriPhyn iVDGL EDG EGEE PPDG GridPP will customize core Grid technology for accelerator-based experiments• Transport Data• Cache Data• Manage initial data analysis and Monte Carlo

Not clear if GT2, GT3, OGSI but will certainly be Web Service based

Need to keep in close touch with these activities Build GlueX physics analysis consistent with this

infrastructure

2020

Implementing Grids Need to design a service architecture for GlueX

• Build on services from HEP and other fields

• Need some specific gluexML meta-data specifying services and properties specific to GlueX

• Specify data structures and method interfaces in XML Use portlets for user-interfaces as in http://www.ogce.org Break-up into services where-ever possible but only if

“coarse-grain”

Module A

Module B

Method Calls.001 to 1 millisecond

Service A

Service B

Messages

0.1 to 1000 millisecond latency

Coarse Grain Service ModelClosely coupled Java/Python …

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Collage of Portals

Earthquakes – NASAFusion – DoEOGCE Components – NSFPublications -- CGL

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Approach Convert every code into a Web Service Convert every utility like

“visualization” into a Web service Have good support for authoring and

manipulating meta-data Use existing code/database technology

(SQL/Fortran/C++) linked to “Application Web/OGSA services”

• XML specification of models, computational steering, scale supported at “Web Service” level as don’t need “high performance” here

• Allows use of Semantic Grid technology

Typicalcodes

WS linkingto user andOther WS

(data sources)

Application WS

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Raw Data and Compute Resources

Middleware

Database

PortalServices

SystemServices

SystemServices

SystemServices

VisualizationServiceModeling

ServicesFittingService

GridComputing

Environments

UserServices

“Core”Grid

(Globus)

Data AccessService

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CERN LHC Data Analysis Grid